UWSpace will be migrating to a new version of its software from July 29th to August 1st. UWSpace will be offline for all UW community members during this time.

Show simple item record

dc.contributor.authorWang, Shiqian
dc.date.accessioned2016-10-11 13:44:01 (GMT)
dc.date.available2016-10-11 13:44:01 (GMT)
dc.date.issued2016-10-11
dc.date.submitted2016-10-01
dc.identifier.urihttp://hdl.handle.net/10012/10988
dc.description.abstractthe road environment. Thus, effective methods are needed for the MLS data processing. The main goal of this thesis is to establish a feasible workflow by testing a series of methods to extract geometrical information of roadside trees from the MLS-acquired point clouds. The workflow developed in this study consists of three parts. The first part deals with ground point removal. As such, only off-ground points are used to extract trees. The second part handles tree detection by comparing four segmentation and clustering methods: the Euclidian distance clustering algorithm, the region growing segmentation method, the normalized cut (Ncut) method, and the supervoxel-based tree detection method. The third part focuses on automated extraction of tree geometric parameters such as tree height, DBH, crown spread, and horizontal slices features. Finally, classification of tree species was conducted using the k-Nearest Neighbour (k-NN) and the random forests (RF) algorithm. A total of four MLS datasets (three in Xiamen, China and one in Kingston, Ontario) acquired in iv 2013 and 2015, respectively, were used to test the developed method. The ground truthing data of DBH estimation were obtained through manual measurement of selected roadside trees after the two MLS missions in Xiamen in the fall 2015. The field surveyed DBH values of the 163 roadside trees were used to estimate the accuracy of the proposed tree extraction method. The 200 manually labeled trees with 8 different species were selected to examine accuracy of the proposed classification method. The results show that over 90% of the roadside trees were correctly detected, with an average error of about 5% in DBH estimation when compared to the field survey, and an overall accuracy of 78% for the classification of tree species.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectreomote sensingen
dc.subjectLiDARen
dc.subjecturban treesen
dc.titleUrban Roadside Tree Inventory Using a Mobile Laser Scanning Systemen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentGeography and Environmental Managementen
uws-etd.degree.disciplineGeographyen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Scienceen
uws.contributor.advisorLi, Jonathan
uws.contributor.affiliation1Faculty of Environmenten
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record


UWSpace

University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages